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Record W2060952403 · doi:10.1109/ims3tw.2011.42

Electrodes Architectures for Dielectrophoretic-Based Cells Manipulation in LoCs: Modeling, Simulation and Experimental Results

2011· article· en· W2060952403 on OpenAlexafffund
Mohamed Amine Miled, Antoine Gagne, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC Microsystems
KeywordsDielectrophoresisElectrodeCapacitive sensingFinite element methodFabricationComputer scienceSquare (algebra)Electrode arrayMaterials scienceNanotechnologyOptoelectronicsEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper several electrodes architectures are modeled, implemented and compared in order to optimize the integration of hybrid micro-system for lab-on-a-chips (LoC). This study highlights the limitations of finite element simulations for a hybrid and complex micro-systems and propose new approach to overpass some fabrication constraints in a LoC design. The proposed LoC is based on dielectrophoresis for cell manipulation and capacitive sensing architecture. Particles used in this study has a diameter less than 10m which is close to the electrode dimensions. In this case conventional dielectrophoresis theory has some limitations. In order to handle particles in the range of micrometers, we describe four different electrode architectures which are the conventional square electrode array whose dimensions are 10m × 10m, the 10m width L-shaped electrode, the star-square electrode and the double side L-shaped electrodes. A comparison is presented at the end of the paper to highlight the advantages of each architecture.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.240
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2011
Admission routes2
Has abstractyes

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